Deprecated: Please use consts in: cloud.google.com/go/automl/apiv1/automlpb
const (
ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED = src.ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED
ClassificationType_MULTICLASS = src.ClassificationType_MULTICLASS
ClassificationType_MULTILABEL = src.ClassificationType_MULTILABEL
DocumentDimensions_CENTIMETER = src.DocumentDimensions_CENTIMETER
DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED = src.DocumentDimensions_DOCUMENT_DIMENSION_UNIT_UNSPECIFIED
DocumentDimensions_INCH = src.DocumentDimensions_INCH
DocumentDimensions_POINT = src.DocumentDimensions_POINT
Document_Layout_FORM_FIELD = src.Document_Layout_FORM_FIELD
Document_Layout_FORM_FIELD_CONTENTS = src.Document_Layout_FORM_FIELD_CONTENTS
Document_Layout_FORM_FIELD_NAME = src.Document_Layout_FORM_FIELD_NAME
Document_Layout_PARAGRAPH = src.Document_Layout_PARAGRAPH
Document_Layout_TABLE = src.Document_Layout_TABLE
Document_Layout_TABLE_CELL = src.Document_Layout_TABLE_CELL
Document_Layout_TABLE_HEADER = src.Document_Layout_TABLE_HEADER
Document_Layout_TABLE_ROW = src.Document_Layout_TABLE_ROW
Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED = src.Document_Layout_TEXT_SEGMENT_TYPE_UNSPECIFIED
Document_Layout_TOKEN = src.Document_Layout_TOKEN
Model_DEPLOYED = src.Model_DEPLOYED
Model_DEPLOYMENT_STATE_UNSPECIFIED = src.Model_DEPLOYMENT_STATE_UNSPECIFIED
Model_UNDEPLOYED = src.Model_UNDEPLOYED
)
Deprecated: Please use vars in: cloud.google.com/go/automl/apiv1/automlpb
var (
ClassificationType_name = src.ClassificationType_name
ClassificationType_value = src.ClassificationType_value
DocumentDimensions_DocumentDimensionUnit_name = src.DocumentDimensions_DocumentDimensionUnit_name
DocumentDimensions_DocumentDimensionUnit_value = src.DocumentDimensions_DocumentDimensionUnit_value
Document_Layout_TextSegmentType_name = src.Document_Layout_TextSegmentType_name
Document_Layout_TextSegmentType_value = src.Document_Layout_TextSegmentType_value
File_google_cloud_automl_v1_annotation_payload_proto = src.File_google_cloud_automl_v1_annotation_payload_proto
File_google_cloud_automl_v1_annotation_spec_proto = src.File_google_cloud_automl_v1_annotation_spec_proto
File_google_cloud_automl_v1_classification_proto = src.File_google_cloud_automl_v1_classification_proto
File_google_cloud_automl_v1_data_items_proto = src.File_google_cloud_automl_v1_data_items_proto
File_google_cloud_automl_v1_dataset_proto = src.File_google_cloud_automl_v1_dataset_proto
File_google_cloud_automl_v1_detection_proto = src.File_google_cloud_automl_v1_detection_proto
File_google_cloud_automl_v1_geometry_proto = src.File_google_cloud_automl_v1_geometry_proto
File_google_cloud_automl_v1_image_proto = src.File_google_cloud_automl_v1_image_proto
File_google_cloud_automl_v1_io_proto = src.File_google_cloud_automl_v1_io_proto
File_google_cloud_automl_v1_model_evaluation_proto = src.File_google_cloud_automl_v1_model_evaluation_proto
File_google_cloud_automl_v1_model_proto = src.File_google_cloud_automl_v1_model_proto
File_google_cloud_automl_v1_operations_proto = src.File_google_cloud_automl_v1_operations_proto
File_google_cloud_automl_v1_prediction_service_proto = src.File_google_cloud_automl_v1_prediction_service_proto
File_google_cloud_automl_v1_service_proto = src.File_google_cloud_automl_v1_service_proto
File_google_cloud_automl_v1_text_extraction_proto = src.File_google_cloud_automl_v1_text_extraction_proto
File_google_cloud_automl_v1_text_proto = src.File_google_cloud_automl_v1_text_proto
File_google_cloud_automl_v1_text_segment_proto = src.File_google_cloud_automl_v1_text_segment_proto
File_google_cloud_automl_v1_text_sentiment_proto = src.File_google_cloud_automl_v1_text_sentiment_proto
File_google_cloud_automl_v1_translation_proto = src.File_google_cloud_automl_v1_translation_proto
Model_DeploymentState_name = src.Model_DeploymentState_name
Model_DeploymentState_value = src.Model_DeploymentState_value
)
func RegisterAutoMlServer(s *grpc.Server, srv AutoMlServer)
Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1/automlpb
func RegisterPredictionServiceServer(s *grpc.Server, srv PredictionServiceServer)
Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1/automlpb
Contains annotation information that is relevant to AutoML.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type AnnotationPayload = src.AnnotationPayload
type AnnotationPayload_Classification = src.AnnotationPayload_Classification
type AnnotationPayload_ImageObjectDetection = src.AnnotationPayload_ImageObjectDetection
type AnnotationPayload_TextExtraction = src.AnnotationPayload_TextExtraction
type AnnotationPayload_TextSentiment = src.AnnotationPayload_TextSentiment
type AnnotationPayload_Translation = src.AnnotationPayload_Translation
A definition of an annotation spec.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type AnnotationSpec = src.AnnotationSpec
AutoMlClient is the client API for AutoMl service. For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type AutoMlClient = src.AutoMlClient
func NewAutoMlClient(cc grpc.ClientConnInterface) AutoMlClient
Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1/automlpb
AutoMlServer is the server API for AutoMl service.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type AutoMlServer = src.AutoMlServer
Input configuration for BatchPredict Action. The format of input depends on the ML problem of the model used for prediction. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise. The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are: <h4>AutoML Vision</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output. Sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png </section><section><h5>Object Detection</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output. Sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png </section> </div> <h4>AutoML Video Intelligence</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END `GCS_FILE_PATH` is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. `TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the length of the video, and the end time must be after the start time. Sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf </section><section><h5>Object Tracking</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END `GCS_FILE_PATH` is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. `TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the length of the video, and the end time must be after the start time. Sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf </section> </div> <h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH `GCS_FILE_PATH` is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF Text files can be no larger than 10MB in size. Sample rows: gs://folder/text1.txt gs://folder/text2.pdf gs://folder/text3.tif </section><section><h5>Sentiment Analysis</h5> One or more CSV files where each line is a single column: GCS_FILE_PATH `GCS_FILE_PATH` is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF Text files can be no larger than 128kB in size. Sample rows: gs://folder/text1.txt gs://folder/text2.pdf gs://folder/text3.tif </section><section><h5>Entity Extraction</h5> One or more JSONL (JSON Lines) files that either provide inline text or documents. You can only use one format, either inline text or documents, for a single call to [AutoMl.BatchPredict]. Each JSONL file contains a per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called "id", a TextSnippet proto (in JSON representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique. Each document JSONL file contains, per line, a proto that wraps a Document proto with `input_config` set. Each document cannot exceed 2MB in size. Supported document extensions: .PDF, .TIF, .TIFF Each JSONL file must not exceed 100MB in size, and no more than 20 JSONL files may be passed. Sample inline JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".): { "id": "my_first_id", "text_snippet": { "content": "dog car cat"}, "text_features": [ { "text_segment": {"start_offset": 4, "end_offset": 6}, "structural_type": PARAGRAPH, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ] }, } ], }\n { "id": "2", "text_snippet": { "content": "Extended sample content", "mime_type": "text/plain" } } Sample document JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by "\n".): { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } } </section> </div> <h4>AutoML Tables</h4><div class="ui-datasection-main"><section class="selected"> See [Preparing your training data](https://cloud.google.com/automl-tables/docs/predict-batch) for more information. You can use either [gcs_source][google.cloud.automl.v1.BatchPredictInputConfig.gcs_source] or [bigquery_source][BatchPredictInputConfig.bigquery_source]. **For gcs_source:** CSV file(s), each by itself 10GB or smaller and total size must be 100GB or smaller, where first file must have a header containing column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. The column names must contain the model's [input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows, i.e. the CSV lines, will be attempted. Sample rows from a CSV file: <pre> "First Name","Last Name","Dob","Addresses" "John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]" "Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} </pre> **For bigquery_source:** The URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. The column names must contain the model's [input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.ColumnSpec.display_name] (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows of the table will be attempted. </section> </div> **Input field definitions:** `GCS_FILE_PATH` : The path to a file on Google Cloud Storage. For example, "gs://folder/video.avi". `TIME_SEGMENT_START` : (`TIME_OFFSET`) Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). `TIME_SEGMENT_END` : (`TIME_OFFSET`) Expresses an end, exclusive, of a time segment within n example that has a time dimension (e.g. video). `TIME_OFFSET` : A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is allowed, and it means the end of the example. **Errors:** If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and prediction does not happen. Regardless of overall success or failure the per-row failures, up to a certain count cap, will be listed in Operation.metadata.partial_failures.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type BatchPredictInputConfig = src.BatchPredictInputConfig
type BatchPredictInputConfig_GcsSource = src.BatchPredictInputConfig_GcsSource
Details of BatchPredict operation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type BatchPredictOperationMetadata = src.BatchPredictOperationMetadata
Further describes this batch predict's output. Supplements BatchPredictOutputConfig[google.cloud.automl.v1.BatchPredictOutputConfig].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type BatchPredictOperationMetadata_BatchPredictOutputInfo = src.BatchPredictOperationMetadata_BatchPredictOutputInfo
type BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory = src.BatchPredictOperationMetadata_BatchPredictOutputInfo_GcsOutputDirectory
Output configuration for BatchPredict Action. As destination the [gcs_destination][google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination] must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be "prediction-<model-display-name>-<timestamp-of-prediction-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for. - For Image Classification: In the created directory files `image_classification_1.jsonl`, `image_classification_2.jsonl`,...,`image_classification_N.jsonl` will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl` files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly one [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only `code` and `message`fields. - For Image Object Detection: In the created directory files `image_object_detection_1.jsonl`, `image_object_detection_2.jsonl`,...,`image_object_detection_N.jsonl` will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl` files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly one [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only `code` and `message`fields. - For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video classification requested in the input (i.e. each line in given CSV(s)), will be created. The format of video_classification.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_classification.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for the video time segment the file is assigned to in the video_classification.csv. All AnnotationPayload protos will have video_classification field set, and will be sorted by video_classification.type field (note that the returned types are governed by `classifaction_types` parameter in [PredictService.BatchPredictRequest.params][]). - For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,..., video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)). The format of video_object_tracking.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for each frame of the video time segment the file is assigned to in video_object_tracking.csv. All AnnotationPayload protos will have video_object_tracking field set. - For Text Classification: In the created directory files `text_classification_1.jsonl`, `text_classification_2.jsonl`,...,`text_classification_N.jsonl` will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl` files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly one [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only `code` and `message`. - For Text Sentiment: In the created directory files `text_sentiment_1.jsonl`, `text_sentiment_2.jsonl`,...,`text_sentiment_N.jsonl` will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have text_sentiment detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl` files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly one [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only `code` and `message`. - For Text Extraction: In the created directory files `text_extraction_1.jsonl`, `text_extraction_2.jsonl`,...,`text_extraction_N.jsonl` will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet's "id" (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl` files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the "id" : "<id_value>" (in case of inline) or the document proto (in case of document) but here followed by exactly one [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only `code` and `message`. - For Tables: Output depends on whether [gcs_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.gcs_destination] or [bigquery_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.bigquery_destination] is set (either is allowed). Google Cloud Storage case: In the created directory files `tables_1.csv`, `tables_2.csv`,..., `tables_N.csv` will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns' [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] given on input followed by M target column names in the format of "<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>_<target value>_score" where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction [scores][google.cloud.automl.v1p1beta.TablesAnnotation.score]. For REGRESSION and FORECASTING [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]: Each .csv file will contain a header, listing all columns' [display_name-s][google.cloud.automl.v1p1beta.display_name] given on input followed by the predicted target column with name in the format of "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>" Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additional `errors_1.csv`, `errors_2.csv`,..., `errors_N.csv` will be created (N depends on total number of failed rows). These files will have analogous format as `tables_*.csv`, but always with a single target column having [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a JSON string, and containing only `code` and `message`. BigQuery case: [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name `prediction_<model-display-name>_<timestamp-of-prediction-call>` where <model-display-name> will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, `predictions`, and `errors`. The `predictions` table's column names will be the input columns' [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name] followed by the target column with name in the format of "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>" The input feature columns will contain the respective values of successfully predicted rows, with the target column having an ARRAY of [AnnotationPayloads][google.cloud.automl.v1p1beta.AnnotationPayload], represented as STRUCT-s, containing [TablesAnnotation][google.cloud.automl.v1p1beta.TablesAnnotation]. The `errors` table contains rows for which the prediction has failed, it has analogous input columns while the target column name is in the format of "errors_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec] [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>", and as a value has [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a STRUCT, and containing only `code` and `message`.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type BatchPredictOutputConfig = src.BatchPredictOutputConfig
type BatchPredictOutputConfig_GcsDestination = src.BatchPredictOutputConfig_GcsDestination
Request message for [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type BatchPredictRequest = src.BatchPredictRequest
Result of the Batch Predict. This message is returned in [response][google.longrunning.Operation.response] of the operation returned by the [PredictionService.BatchPredict][google.cloud.automl.v1.PredictionService.BatchPredict].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type BatchPredictResult = src.BatchPredictResult
Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type BoundingBoxMetricsEntry = src.BoundingBoxMetricsEntry
Metrics for a single confidence threshold.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type BoundingBoxMetricsEntry_ConfidenceMetricsEntry = src.BoundingBoxMetricsEntry_ConfidenceMetricsEntry
A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type BoundingPoly = src.BoundingPoly
Contains annotation details specific to classification.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ClassificationAnnotation = src.ClassificationAnnotation
Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ClassificationEvaluationMetrics = src.ClassificationEvaluationMetrics
Metrics for a single confidence threshold.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ClassificationEvaluationMetrics_ConfidenceMetricsEntry = src.ClassificationEvaluationMetrics_ConfidenceMetricsEntry
Confusion matrix of the model running the classification.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ClassificationEvaluationMetrics_ConfusionMatrix = src.ClassificationEvaluationMetrics_ConfusionMatrix
Output only. A row in the confusion matrix.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ClassificationEvaluationMetrics_ConfusionMatrix_Row = src.ClassificationEvaluationMetrics_ConfusionMatrix_Row
Type of the classification problem.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ClassificationType = src.ClassificationType
Details of CreateDataset operation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type CreateDatasetOperationMetadata = src.CreateDatasetOperationMetadata
Request message for [AutoMl.CreateDataset][google.cloud.automl.v1.AutoMl.CreateDataset].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type CreateDatasetRequest = src.CreateDatasetRequest
Details of CreateModel operation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type CreateModelOperationMetadata = src.CreateModelOperationMetadata
Request message for [AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type CreateModelRequest = src.CreateModelRequest
A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type Dataset = src.Dataset
type Dataset_ImageClassificationDatasetMetadata = src.Dataset_ImageClassificationDatasetMetadata
type Dataset_ImageObjectDetectionDatasetMetadata = src.Dataset_ImageObjectDetectionDatasetMetadata
type Dataset_TextClassificationDatasetMetadata = src.Dataset_TextClassificationDatasetMetadata
type Dataset_TextExtractionDatasetMetadata = src.Dataset_TextExtractionDatasetMetadata
type Dataset_TextSentimentDatasetMetadata = src.Dataset_TextSentimentDatasetMetadata
type Dataset_TranslationDatasetMetadata = src.Dataset_TranslationDatasetMetadata
Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1.AutoMl.DeleteDataset].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type DeleteDatasetRequest = src.DeleteDatasetRequest
Request message for [AutoMl.DeleteModel][google.cloud.automl.v1.AutoMl.DeleteModel].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type DeleteModelRequest = src.DeleteModelRequest
Details of operations that perform deletes of any entities.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type DeleteOperationMetadata = src.DeleteOperationMetadata
Details of DeployModel operation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type DeployModelOperationMetadata = src.DeployModelOperationMetadata
Request message for [AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type DeployModelRequest = src.DeployModelRequest
type DeployModelRequest_ImageClassificationModelDeploymentMetadata = src.DeployModelRequest_ImageClassificationModelDeploymentMetadata
type DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata = src.DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata
A structured text document e.g. a PDF.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type Document = src.Document
Message that describes dimension of a document.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type DocumentDimensions = src.DocumentDimensions
Unit of the document dimension.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type DocumentDimensions_DocumentDimensionUnit = src.DocumentDimensions_DocumentDimensionUnit
Input configuration of a Document[google.cloud.automl.v1.Document].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type DocumentInputConfig = src.DocumentInputConfig
Describes the layout information of a [text_segment][google.cloud.automl.v1.Document.Layout.text_segment] in the document.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type Document_Layout = src.Document_Layout
The type of TextSegment in the context of the original document.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type Document_Layout_TextSegmentType = src.Document_Layout_TextSegmentType
Example data used for training or prediction.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ExamplePayload = src.ExamplePayload
type ExamplePayload_Document = src.ExamplePayload_Document
type ExamplePayload_Image = src.ExamplePayload_Image
type ExamplePayload_TextSnippet = src.ExamplePayload_TextSnippet
Details of ExportData operation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ExportDataOperationMetadata = src.ExportDataOperationMetadata
Further describes this export data's output. Supplements OutputConfig[google.cloud.automl.v1.OutputConfig].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ExportDataOperationMetadata_ExportDataOutputInfo = src.ExportDataOperationMetadata_ExportDataOutputInfo
type ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory = src.ExportDataOperationMetadata_ExportDataOutputInfo_GcsOutputDirectory
Request message for [AutoMl.ExportData][google.cloud.automl.v1.AutoMl.ExportData].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ExportDataRequest = src.ExportDataRequest
Details of ExportModel operation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ExportModelOperationMetadata = src.ExportModelOperationMetadata
Further describes the output of model export. Supplements ModelExportOutputConfig[google.cloud.automl.v1.ModelExportOutputConfig].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ExportModelOperationMetadata_ExportModelOutputInfo = src.ExportModelOperationMetadata_ExportModelOutputInfo
Request message for [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]. Models need to be enabled for exporting, otherwise an error code will be returned.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ExportModelRequest = src.ExportModelRequest
The Google Cloud Storage location where the output is to be written to.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type GcsDestination = src.GcsDestination
The Google Cloud Storage location for the input content.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type GcsSource = src.GcsSource
Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type GetAnnotationSpecRequest = src.GetAnnotationSpecRequest
Request message for [AutoMl.GetDataset][google.cloud.automl.v1.AutoMl.GetDataset].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type GetDatasetRequest = src.GetDatasetRequest
Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type GetModelEvaluationRequest = src.GetModelEvaluationRequest
Request message for [AutoMl.GetModel][google.cloud.automl.v1.AutoMl.GetModel].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type GetModelRequest = src.GetModelRequest
A representation of an image. Only images up to 30MB in size are supported.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type Image = src.Image
Dataset metadata that is specific to image classification.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImageClassificationDatasetMetadata = src.ImageClassificationDatasetMetadata
Model deployment metadata specific to Image Classification.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImageClassificationModelDeploymentMetadata = src.ImageClassificationModelDeploymentMetadata
Model metadata for image classification.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImageClassificationModelMetadata = src.ImageClassificationModelMetadata
Annotation details for image object detection.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImageObjectDetectionAnnotation = src.ImageObjectDetectionAnnotation
Dataset metadata specific to image object detection.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImageObjectDetectionDatasetMetadata = src.ImageObjectDetectionDatasetMetadata
Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImageObjectDetectionEvaluationMetrics = src.ImageObjectDetectionEvaluationMetrics
Model deployment metadata specific to Image Object Detection.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImageObjectDetectionModelDeploymentMetadata = src.ImageObjectDetectionModelDeploymentMetadata
Model metadata specific to image object detection.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImageObjectDetectionModelMetadata = src.ImageObjectDetectionModelMetadata
type Image_ImageBytes = src.Image_ImageBytes
Details of ImportData operation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImportDataOperationMetadata = src.ImportDataOperationMetadata
Request message for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ImportDataRequest = src.ImportDataRequest
Input configuration for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] action. The format of input depends on dataset_metadata the Dataset into which the import is happening has. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise. Additionally any input .CSV file by itself must be 100MB or smaller, unless specified otherwise. If an "example" file (that is, image, video etc.) with identical content (even if it had different `GCS_FILE_PATH`) is mentioned multiple times, then its label, bounding boxes etc. are appended. The same file should be always provided with the same `ML_USE` and `GCS_FILE_PATH`, if it is not, then these values are nondeterministically selected from the given ones. The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are: <h4>AutoML Vision</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> See [Preparing your training data](https://cloud.google.com/vision/automl/docs/prepare) for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,LABEL,LABEL,... * `ML_USE` - Identifies the data set that the current row (file) applies to. This value can be one of the following: * `TRAIN` - Rows in this file are used to train the model. * `TEST` - Rows in this file are used to test the model during training. * `UNASSIGNED` - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing. - `GCS_FILE_PATH` - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO. * `LABEL` - A label that identifies the object in the image. For the `MULTICLASS` classification type, at most one `LABEL` is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with no `LABEL`. Some sample rows: TRAIN,gs://folder/image1.jpg,daisy TEST,gs://folder/image2.jpg,dandelion,tulip,rose UNASSIGNED,gs://folder/image3.jpg,daisy UNASSIGNED,gs://folder/image4.jpg </section><section><h5>Object Detection</h5> See [Preparing your training data](https://cloud.google.com/vision/automl/object-detection/docs/prepare) for more information. A CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,) * `ML_USE` - Identifies the data set that the current row (file) applies to. This value can be one of the following: * `TRAIN` - Rows in this file are used to train the model. * `TEST` - Rows in this file are used to test the model during training. * `UNASSIGNED` - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing. - `GCS_FILE_PATH` - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled. - `LABEL` - A label that identifies the object in the image specified by the `BOUNDING_BOX`. - `BOUNDING BOX` - The vertices of an object in the example image. The minimum allowed `BOUNDING_BOX` edge length is 0.01, and no more than 500 `BOUNDING_BOX` instances per image are allowed (one `BOUNDING_BOX` per line). If an image has no looked for objects then it should be mentioned just once with no LABEL and the ",,,,,,," in place of the `BOUNDING_BOX`. **Four sample rows:** TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,, TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,, UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3 TEST,gs://folder/im3.png,,,,,,,,, </section> </div> <h4>AutoML Video Intelligence</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> See [Preparing your training data](https://cloud.google.com/video-intelligence/automl/docs/prepare) for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH For `ML_USE`, do not use `VALIDATE`. `GCS_FILE_PATH` is the path to another .csv file that describes training example for a given `ML_USE`, using the following row format: GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,) Here `GCS_FILE_PATH` leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. `TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the length of the video, and the end time must be after the start time. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it should be mentioned just once with ",," in place of `LABEL, TIME_SEGMENT_START,TIME_SEGMENT_END`. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,120,180.000021 gs://folder/video1.avi,bike,150,180.000021 gs://folder/vid2.avi,car,0,60.5 gs://folder/vid3.avi,,, </section><section><h5>Object Tracking</h5> See [Preparing your training data](/video-intelligence/automl/object-tracking/docs/prepare) for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH For `ML_USE`, do not use `VALIDATE`. `GCS_FILE_PATH` is the path to another .csv file that describes training example for a given `ML_USE`, using the following row format: GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or GCS_FILE_PATH,,,,,,,,,, Here `GCS_FILE_PATH` leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. Providing `INSTANCE_ID`s can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the same `INSTANCE_ID` is given to it. `TIMESTAMP` must be within the length of the video, the `BOUNDING_BOX` is assumed to be drawn on the closest video's frame to the `TIMESTAMP`. Any mentioned by the `TIMESTAMP` frame is expected to be exhaustively labeled and no more than 500 `BOUNDING_BOX`-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with ",,,,,,,,,," in place of `LABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX`. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9 gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9 gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3 gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,, gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,, gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,, gs://folder/video2.avi,,,,,,,,,,, </section> </div> <h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Entity Extraction</h5> See [Preparing your training data](/natural-language/automl/entity-analysis/docs/prepare) for more information. One or more CSV file(s) with each line in the following format: ML_USE,GCS_FILE_PATH * `ML_USE` - Identifies the data set that the current row (file) applies to. This value can be one of the following: * `TRAIN` - Rows in this file are used to train the model. * `TEST` - Rows in this file are used to test the model during training. * `UNASSIGNED` - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.. - `GCS_FILE_PATH` - a Identifies JSON Lines (.JSONL) file stored in Google Cloud Storage that contains in-line text in-line as documents for model training. After the training data set has been determined from the `TRAIN` and `UNASSIGNED` CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation. For example: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl **In-line JSONL files** In-line .JSONL files contain, per line, a JSON document that wraps a [`text_snippet`][google.cloud.automl.v1.TextSnippet] field followed by one or more [`annotations`][google.cloud.automl.v1.AnnotationPayload] fields, which have `display_name` and `text_extraction` fields to describe the entity from the text snippet. Multiple JSON documents can be separated using line breaks (\n). The supplied text must be annotated exhaustively. For example, if you include the text "horse", but do not label it as "animal", then "horse" is assumed to not be an "animal". Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded. ASCII is accepted as it is UTF-8 NFC encoded. For example: { "text_snippet": { "content": "dog car cat" }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 2} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 6} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 10} } } ] }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 7} } } ] } **JSONL files that reference documents** .JSONL files contain, per line, a JSON document that wraps a `input_config` that contains the path to a source document. Multiple JSON documents can be separated using line breaks (\n). Supported document extensions: .PDF, .TIF, .TIFF For example: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } } **In-line JSONL files with document layout information** **Note:** You can only annotate documents using the UI. The format described below applies to annotated documents exported using the UI or `exportData`. In-line .JSONL files for documents contain, per line, a JSON document that wraps a `document` field that provides the textual content of the document and the layout information. For example: { "document": { "document_text": { "content": "dog car cat" } "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 11, }, "page_number": 1, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ], }, "text_segment_type": TOKEN, } ], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 3, }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 3} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 7} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 11} } }, ], </section><section><h5>Classification</h5> See [Preparing your training data](https://cloud.google.com/natural-language/automl/docs/prepare) for more information. One or more CSV file(s) with each line in the following format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... * `ML_USE` - Identifies the data set that the current row (file) applies to. This value can be one of the following: * `TRAIN` - Rows in this file are used to train the model. * `TEST` - Rows in this file are used to test the model during training. * `UNASSIGNED` - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing. - `TEXT_SNIPPET` and `GCS_FILE_PATH` are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as a `GCS_FILE_PATH`. Otherwise, if the content is enclosed in double quotes (""), it is treated as a `TEXT_SNIPPET`. For `GCS_FILE_PATH`, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. For `TEXT_SNIPPET`, AutoML imports the column content excluding quotes. In both cases, size of the content must be 10MB or less in size. For zip files, the size of each file inside the zip must be 10MB or less in size. For the `MULTICLASS` classification type, at most one `LABEL` is allowed. The `ML_USE` and `LABEL` columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP A maximum of 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood gs://folder/content.txt,SlowService TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,BadFood </section><section><h5>Sentiment Analysis</h5> See [Preparing your training data](https://cloud.google.com/natural-language/automl/docs/prepare) for more information. CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT * `ML_USE` - Identifies the data set that the current row (file) applies to. This value can be one of the following: * `TRAIN` - Rows in this file are used to train the model. * `TEST` - Rows in this file are used to test the model during training. * `UNASSIGNED` - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing. - `TEXT_SNIPPET` and `GCS_FILE_PATH` are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as a `GCS_FILE_PATH`. Otherwise, if the content is enclosed in double quotes (""), it is treated as a `TEXT_SNIPPET`. For `GCS_FILE_PATH`, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. For `TEXT_SNIPPET`, AutoML imports the column content excluding quotes. In both cases, size of the content must be 128kB or less in size. For zip files, the size of each file inside the zip must be 128kB or less in size. The `ML_USE` and `SENTIMENT` columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP - `SENTIMENT` - An integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive). Describes the ordinal of the sentiment - higher value means a more positive sentiment. All the values are completely relative, i.e. neither 0 needs to mean a negative or neutral sentiment nor sentiment_max needs to mean a positive one - it is just required that 0 is the least positive sentiment in the data, and sentiment_max is the most positive one. The SENTIMENT shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API. All SENTIMENT values between 0 and sentiment_max must be represented in the imported data. On prediction the same 0 to sentiment_max range will be used. The difference between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 may be similar whereas the difference between 2 and 3 may be large. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 gs://folder/content.txt,3 TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,2 </section> </div> <h4>AutoML Tables</h4><div class="ui-datasection-main"><section class="selected"> See [Preparing your training data](https://cloud.google.com/automl-tables/docs/prepare) for more information. You can use either [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] or [bigquery_source][google.cloud.automl.v1.InputConfig.bigquery_source]. All input is concatenated into a single [primary_table_spec_id][google.cloud.automl.v1.TablesDatasetMetadata.primary_table_spec_id] **For gcs_source:** CSV file(s), where the first row of the first file is the header, containing unique column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller. First three sample rows of a CSV file: <pre> "Id","First Name","Last Name","Dob","Addresses" "1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]" "2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} </pre> **For bigquery_source:** An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel. </section> </div> **Input field definitions:** `ML_USE` : ("TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED") Describes how the given example (file) should be used for model training. "UNASSIGNED" can be used when user has no preference. `GCS_FILE_PATH` : The path to a file on Google Cloud Storage. For example, "gs://folder/image1.png". `LABEL` : A display name of an object on an image, video etc., e.g. "dog". Must be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9. For each label an AnnotationSpec is created which display_name becomes the label; AnnotationSpecs are given back in predictions. `INSTANCE_ID` : A positive integer that identifies a specific instance of a labeled entity on an example. Used e.g. to track two cars on a video while being able to tell apart which one is which. `BOUNDING_BOX` : (`VERTEX,VERTEX,VERTEX,VERTEX` | `VERTEX,,,VERTEX,,`) A rectangle parallel to the frame of the example (image, video). If 4 vertices are given they are connected by edges in the order provided, if 2 are given they are recognized as diagonally opposite vertices of the rectangle. `VERTEX` : (`COORDINATE,COORDINATE`) First coordinate is horizontal (x), the second is vertical (y). `COORDINATE` : A float in 0 to 1 range, relative to total length of image or video in given dimension. For fractions the leading non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is in top left. `TIME_SEGMENT_START` : (`TIME_OFFSET`) Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). `TIME_SEGMENT_END` : (`TIME_OFFSET`) Expresses an end, exclusive, of a time segment within n example that has a time dimension (e.g. video). `TIME_OFFSET` : A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is allowed, and it means the end of the example. `TEXT_SNIPPET` : The content of a text snippet, UTF-8 encoded, enclosed within double quotes (""). `DOCUMENT` : A field that provides the textual content with document and the layout information. **Errors:** If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and nothing is imported. Regardless of overall success or failure the per-row failures, up to a certain count cap, is listed in Operation.metadata.partial_failures.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type InputConfig = src.InputConfig
type InputConfig_GcsSource = src.InputConfig_GcsSource
Request message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ListDatasetsRequest = src.ListDatasetsRequest
Response message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ListDatasetsResponse = src.ListDatasetsResponse
Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ListModelEvaluationsRequest = src.ListModelEvaluationsRequest
Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ListModelEvaluationsResponse = src.ListModelEvaluationsResponse
Request message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ListModelsRequest = src.ListModelsRequest
Response message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ListModelsResponse = src.ListModelsResponse
API proto representing a trained machine learning model.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type Model = src.Model
Evaluation results of a model.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ModelEvaluation = src.ModelEvaluation
type ModelEvaluation_ClassificationEvaluationMetrics = src.ModelEvaluation_ClassificationEvaluationMetrics
type ModelEvaluation_ImageObjectDetectionEvaluationMetrics = src.ModelEvaluation_ImageObjectDetectionEvaluationMetrics
type ModelEvaluation_TextExtractionEvaluationMetrics = src.ModelEvaluation_TextExtractionEvaluationMetrics
type ModelEvaluation_TextSentimentEvaluationMetrics = src.ModelEvaluation_TextSentimentEvaluationMetrics
type ModelEvaluation_TranslationEvaluationMetrics = src.ModelEvaluation_TranslationEvaluationMetrics
Output configuration for ModelExport Action.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type ModelExportOutputConfig = src.ModelExportOutputConfig
type ModelExportOutputConfig_GcsDestination = src.ModelExportOutputConfig_GcsDestination
Deployment state of the model.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type Model_DeploymentState = src.Model_DeploymentState
type Model_ImageClassificationModelMetadata = src.Model_ImageClassificationModelMetadata
type Model_ImageObjectDetectionModelMetadata = src.Model_ImageObjectDetectionModelMetadata
type Model_TextClassificationModelMetadata = src.Model_TextClassificationModelMetadata
type Model_TextExtractionModelMetadata = src.Model_TextExtractionModelMetadata
type Model_TextSentimentModelMetadata = src.Model_TextSentimentModelMetadata
type Model_TranslationModelMetadata = src.Model_TranslationModelMetadata
A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type NormalizedVertex = src.NormalizedVertex
Metadata used across all long running operations returned by AutoML API.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type OperationMetadata = src.OperationMetadata
type OperationMetadata_BatchPredictDetails = src.OperationMetadata_BatchPredictDetails
type OperationMetadata_CreateDatasetDetails = src.OperationMetadata_CreateDatasetDetails
type OperationMetadata_CreateModelDetails = src.OperationMetadata_CreateModelDetails
type OperationMetadata_DeleteDetails = src.OperationMetadata_DeleteDetails
type OperationMetadata_DeployModelDetails = src.OperationMetadata_DeployModelDetails
type OperationMetadata_ExportDataDetails = src.OperationMetadata_ExportDataDetails
type OperationMetadata_ExportModelDetails = src.OperationMetadata_ExportModelDetails
type OperationMetadata_ImportDataDetails = src.OperationMetadata_ImportDataDetails
type OperationMetadata_UndeployModelDetails = src.OperationMetadata_UndeployModelDetails
- For Translation: CSV file `translation.csv`, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes examples that have given ML_USE, using the following row format per line: TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target language) - For Tables: Output depends on whether the dataset was imported from Google Cloud Storage or BigQuery. Google Cloud Storage case: [gcs_destination][google.cloud.automl.v1p1beta.OutputConfig.gcs_destination] must be set. Exported are CSV file(s) `tables_1.csv`, `tables_2.csv`,...,`tables_N.csv` with each having as header line the table's column names, and all other lines contain values for the header columns. BigQuery case: [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination] pointing to a BigQuery project must be set. In the given project a new dataset will be created with name `export_data_<automl-dataset-display-name>_<timestamp-of-export-call>` where <automl-dataset-display-name> will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that dataset a new table called `primary_table` will be created, and filled with precisely the same data as this obtained on import.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type OutputConfig = src.OutputConfig
type OutputConfig_GcsDestination = src.OutputConfig_GcsDestination
Request message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type PredictRequest = src.PredictRequest
Response message for [PredictionService.Predict][google.cloud.automl.v1.PredictionService.Predict].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type PredictResponse = src.PredictResponse
PredictionServiceClient is the client API for PredictionService service. For semantics around ctx use and closing/ending streaming RPCs, please refer to https://godoc.org/google.golang.org/grpc#ClientConn.NewStream.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type PredictionServiceClient = src.PredictionServiceClient
func NewPredictionServiceClient(cc grpc.ClientConnInterface) PredictionServiceClient
Deprecated: Please use funcs in: cloud.google.com/go/automl/apiv1/automlpb
PredictionServiceServer is the server API for PredictionService service.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type PredictionServiceServer = src.PredictionServiceServer
Dataset metadata for classification.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextClassificationDatasetMetadata = src.TextClassificationDatasetMetadata
Model metadata that is specific to text classification.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextClassificationModelMetadata = src.TextClassificationModelMetadata
Annotation for identifying spans of text.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextExtractionAnnotation = src.TextExtractionAnnotation
type TextExtractionAnnotation_TextSegment = src.TextExtractionAnnotation_TextSegment
Dataset metadata that is specific to text extraction
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextExtractionDatasetMetadata = src.TextExtractionDatasetMetadata
Model evaluation metrics for text extraction problems.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextExtractionEvaluationMetrics = src.TextExtractionEvaluationMetrics
Metrics for a single confidence threshold.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextExtractionEvaluationMetrics_ConfidenceMetricsEntry = src.TextExtractionEvaluationMetrics_ConfidenceMetricsEntry
Model metadata that is specific to text extraction.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextExtractionModelMetadata = src.TextExtractionModelMetadata
A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextSegment = src.TextSegment
Contains annotation details specific to text sentiment.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextSentimentAnnotation = src.TextSentimentAnnotation
Dataset metadata for text sentiment.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextSentimentDatasetMetadata = src.TextSentimentDatasetMetadata
Model evaluation metrics for text sentiment problems.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextSentimentEvaluationMetrics = src.TextSentimentEvaluationMetrics
Model metadata that is specific to text sentiment.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextSentimentModelMetadata = src.TextSentimentModelMetadata
A representation of a text snippet.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TextSnippet = src.TextSnippet
Annotation details specific to translation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TranslationAnnotation = src.TranslationAnnotation
Dataset metadata that is specific to translation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TranslationDatasetMetadata = src.TranslationDatasetMetadata
Evaluation metrics for the dataset.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TranslationEvaluationMetrics = src.TranslationEvaluationMetrics
Model metadata that is specific to translation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type TranslationModelMetadata = src.TranslationModelMetadata
Details of UndeployModel operation.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type UndeployModelOperationMetadata = src.UndeployModelOperationMetadata
Request message for [AutoMl.UndeployModel][google.cloud.automl.v1.AutoMl.UndeployModel].
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type UndeployModelRequest = src.UndeployModelRequest
UnimplementedAutoMlServer can be embedded to have forward compatible implementations.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type UnimplementedAutoMlServer = src.UnimplementedAutoMlServer
UnimplementedPredictionServiceServer can be embedded to have forward compatible implementations.
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type UnimplementedPredictionServiceServer = src.UnimplementedPredictionServiceServer
Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1.AutoMl.UpdateDataset]
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type UpdateDatasetRequest = src.UpdateDatasetRequest
Request message for [AutoMl.UpdateModel][google.cloud.automl.v1.AutoMl.UpdateModel]
Deprecated: Please use types in: cloud.google.com/go/automl/apiv1/automlpb
type UpdateModelRequest = src.UpdateModelRequest